Applied AI ML Lead RISK MODELING

JPMorganChase & Co. is a leading global financial services firm with operations worldwide and a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorganChase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its Company and Chase brands. Information about JPMorganChase & Co. is available at Company website. Chase Consumer & Community Banking serves consumers and small businesses with a broad range of financial services, and its Risk Management organization partners with each sub-line of business to identify, assess, prioritize, and remediate risk. We are currently seeking an Applied AI/Machine Learning Lead for the Risk Modeling team.

As an Applied AI/Machine Learning Lead – Vice President within Chase Consumer & Community Banking Risk Modeling, you will lead the design, development, deployment, and monitoring of AI, machine learning, and statistical models, segmentations, and strategies leveraging big data and distributed computing platforms. Your work will support risk management for the credit card and small business portfolios, helping drive long-term profitable growth through strong business acumen, disciplined execution, and clear communication to senior stakeholders. You will partner closely with business stakeholders and cross-functional teams across the full modeling lifecycle—from problem framing and conceptualization through production deployment, monitoring, and ongoing maintenance. You will serve as a hands-on individual contributor while also mentoring junior team members as needed, helping shape technical direction and establishing modern AI-enabled ways of working, including responsible use of generative AI in core workflows.

Job responsibilities 

• Design and develop sophisticated AI, machine learning, and statistical models to drive impactful business decisions in consumer and small business credit risk (for example, segmentation, underwriting and decisioning support, and portfolio strategies).
• Apply novel approaches and advanced machine learning techniques to solve high-value problems, balancing predictive performance, stability, interpretability (where needed), and operational constraints.
• Leverage big data, distributed computing, and cloud platforms to optimize and accelerate model development processes, enabling scalable feature engineering, training, scoring, and monitoring.
• Partner with senior management to define modeling roadmaps, align solutions to business needs, communicate trade-offs, and drive execution through production delivery.
• Collaborate across the full model lifecycle with key stakeholders in marketing, risk, technology, model governance/model risk management, fair lending, compliance, and other control partners through conceptualization and problem framing; development and testing; review/validation support and documentation; deployment and model usage; and ongoing model monitoring and maintenance.
• Embed AI and generative AI into core workflows (responsibly) to improve speed and quality of delivery, including accelerating prototyping and experimentation; improving developer productivity (code generation/refactoring with strong review and testing discipline); producing high-quality model documentation and artifacts (assumptions, limitations, monitoring plans); and synthesizing results into decision-ready narratives for leadership.
• Promote strong governance and controls by ensuring models are well-documented, reproducible, monitored, and maintained with disciplined change management and clear performance reporting.

Required qualifications, capabilities, and skills 

• Advanced degree in Mathematics, Statistics, Computer Science, Operations Research, Econometrics, Physics, or other related quantitative fields (or equivalent practical experience).
• Minimum 8 years of experience in developing and managing predictive risk models in financial institutions.
• Demonstrated ability to apply AI, including Generative AI, to improve core modeling workflows while maintaining rigorous model risk and control standards.
• Strong programming proficiency (Python, PyTorch or TensorFlow, PySpark, SQL) and ability to write production-quality, maintainable code (version control, testing, reproducibility, and documentation).
• Familiarity with cloud services and distributed computing (for example, AWS SageMaker, Amazon EMR, Spark) to scale model development and operationalization.
• Fundamental understanding of consumer lending business and how models support credit risk decisioning and portfolio strategies.
• Deep understanding of advanced machine learning algorithms (for example, random forest, XGBoost, neural networks) and tuning/validation procedures, including performance evaluation and robustness/stability considerations.
• Effective communication skills with the ability to convey complex modeling concepts and results clearly to senior management and cross-functional stakeholders.

Preferred qualifications, capabilities, and skills 

• Experience designing, building, and deploying production-quality AI and machine learning models end-to-end, including model monitoring/drift and sustainable maintenance practices.
• Expertise in data wrangling and model building on distributed Spark environments with a focus on stability, scalability, and efficiency; GPU experience is a plus.
• Experience supporting governance expectations in regulated environments (model documentation quality, explainability/interpretability as appropriate, monitoring, and change control); experience partnering with Fair Lending stakeholders is a plus.
• Strong ownership and execution skills with a track record of delivering models into production and driving adoption.
• Ability to collaborate across multiple stakeholders on initiatives of strategic importance; comfort leading through influence.
• Exposure to generative AI and large language model patterns (for example, evaluation approaches, retrieval-augmented workflows, safety/quality guardrails) applied pragmatically to analytics/modeling productivity and/or risk use cases.